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    "\n",
    "<p align=\"center\">\n",
    "    <img src=\"https://github.com/GeostatsGuy/GeostatsPy/blob/master/TCG_color_logo.png?raw=true\" width=\"220\" height=\"240\" />\n",
    "\n",
    "</p>\n",
    "\n",
    "## Subsurface Data Analytics \n",
    "\n",
    "### Basic Data Loading and Display for Tabular (DataFrames) and Gridded Data (ndarrays) Structures and in Python \n",
    "\n",
    "#### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "\n",
    "##### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n",
    "\n",
    "### Exercise: Loading and Basic Display \n",
    "\n",
    "Let's just load data and look at it.  We will do much more soon.  \n",
    "\n",
    "* We must learn to walk before we can mountain bike!\n",
    "\n",
    "First let's just describe the data types.\n",
    "\n",
    "#### The Data Types\n",
    "\n",
    "In Python we will commonly store our data in two formats, tables and arrays.  \n",
    "\n",
    "* For sample data with typically multiple features $1,\\ldots,m$ over $1,\\ldots,n$ samples we will work with tables.   \n",
    "\n",
    "* For exhaustive 2D maps and 3D models (usually representing a single feature) on a regular grid over $[1,\\ldots,n_{1}], [1,\\ldots,n_{2}],\\ldots,[1,\\ldots,n_{ndim}]$, where $n_{dim}$ is the number of dimensions, we will work with arrays.\n",
    "\n",
    "* Tabular Data in subsurface data analytics includes any data set with a limited number of samples as oposed to gridded maps that provide exhaustively sampled data.\n",
    "\n",
    "This tutorial includes the methods and operations that would commonly be required for and Geoscientists, Engineers and Data Scientists working with Tabular Data Structures for the purpose of:\n",
    "\n",
    "1. Data Checking and Cleaning\n",
    "2. Data Mining / Inferential Data Analysis\n",
    "3. Data Analytics / Building Predictive Models with Geostatistics and Machine Learning\n",
    "\n",
    "Learning to work with Pandas DataFrames is essential for dealing with tabular data (e.g. well data) in subsurface modeling workflows and for subsurface machine learning.\n",
    "\n",
    "##### Tabular Data Structures\n",
    "\n",
    "In Python we will commonly store our data in two formats, tables and arrays.  For sampled data with typically multiple features $1,\\ldots,m$ over $1,\\ldots,n$ samples we will work with tables.  For exhaustive maps and models usually representing a single feature on a regular grid over $1,\\ldots,n_{i}$ for $i = 1,\\ldots,n_{dim}$ we will work with arrays.\n",
    "\n",
    "pandas package provides a convenient DataFrame object for working with data in a table and numpy package provides a convenient ndarray object for working with gridded data. In the following tutorial we will focus on DataFrames although we will utilize ndarrays a couple of times.  There is another section on Gridded Data Structures that focuses on ndarrays.\n",
    "\n",
    "##### Regular Data Structures\n",
    "\n",
    "For exhaustive 2D maps and 3D models (usually representing a single feature) on a regular grid over $[1,\\ldots,n_{1}], [1,\\ldots,n_{2}],\\ldots,[1,\\ldots,n_{ndim}]$, where $n_{dim}$ is the number of dimensions, we will work with arrays.  Of course, it is always possible to add another dimension to our array to include multiple features, $1,\\ldots,m$, over all locations.\n",
    "\n",
    "In geostatistical workflows the tables are typically sample data from wells and drill holes and the grids are the  interpolated or simulated models or secondary data from sources such as seismic inversion.\n",
    "\n",
    "The NumPy package provides a convenient *ndarray* object for working with regularly gridded data. In the following tutorial we will focus on practical methods with *ndarray*s.  There is another section available on Tabular Data Structures that focuses on DataFrames at https://github.com/GeostatsGuy/PythonNumericalDemos/blob/master/PythonDataBasics_DataFrame.ipynb.\n",
    "\n",
    "#### Additional Resources\n",
    "\n",
    "These workflows are based on standard methods with their associated limitations and assumptions. For more information see:\n",
    "\n",
    "* [Bootstrap Lecture](https://www.youtube.com/watch?v=wCgdoImlLY0)\n",
    "\n",
    "I have provided various workflows for subsurface data analytics, geostatistics and machine learning:\n",
    "\n",
    "* [Python](https://git.io/fh4eX)\n",
    "\n",
    "* [Excel](https://github.com/GeostatsGuy/LectureExercises/blob/master/Lecture7_CI_Hypoth_eg_R.xlsx) \n",
    "* [R](https://github.com/GeostatsGuy/LectureExercises/blob/master/Lecture7_CI_Hypoth_eg.R)  \n",
    "\n",
    "and all of my University of Texas at Austin \n",
    "\n",
    "* [Lectures](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig/featured?view_as=subscriber)\n",
    "\n",
    "#### Workflow Goals\n",
    "\n",
    "Learn the basics for working with Tabular Data Structures in Python. This includes:\n",
    "\n",
    "* Loading tabular data\n",
    "* Visualizing tabular data\n",
    "* Data QC and Cleaning\n",
    "* Interacting with the tabular data\n",
    "\n",
    "#### Objective \n",
    "\n",
    "I want to provide hands-on experience with building subsurface modeling workflows. Python provides an excellent vehicle to accomplish this. I have coded a package called GeostatsPy with GSLIB: Geostatistical Library (Deutsch and Journel, 1998) functionality that provides basic building blocks for building subsurface modeling workflows. \n",
    "\n",
    "The objective is to remove the hurdles of subsurface modeling workflow construction by providing building blocks and sufficient examples. This is not a coding class per se, but we need the ability to 'script' workflows working with numerical methods.    \n",
    "\n",
    "#### Getting Started\n",
    "\n",
    "Here's the steps to get setup in Python with the GeostatsPy package:\n",
    "\n",
    "1. Install Anaconda 3 on your machine (https://www.anaconda.com/download/). \n",
    "2. From Anaconda Navigator (within Anaconda3 group), go to the environment tab, click on base (root) green arrow and open a terminal. \n",
    "3. In the terminal type: pip install geostatspy. \n",
    "4. Open Jupyter and in the top block get started by copy and pasting the code block below from this Jupyter Notebook to start using the geostatspy functionality. \n",
    "\n",
    "You will need to copy the data file to your working directory.  They are available here:\n",
    "\n",
    "* Tabular data - 2D_MV_200wells.csv at [here].(https://github.com/GeostatsGuy/GeoDataSets/blob/master/2D_MV_200wells.csv)\n",
    "\n",
    "I have put together various subsurface workflows for data analytics, geostatistics and machine learning. Go [here](https://git.io/fh4eX) for other example workflows and source code. \n",
    "\n",
    "#### Load the required libraries\n",
    "\n",
    "The following code **imports** the required libraries. After we excute this code we can use 'os', 'np', 'pd' or 'GSLIB' to access functionality in each of these libraries."
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   "source": [
    "import os                                                   # to set current working directory \n",
    "import numpy as np                                          # arrays and matrix math\n",
    "import pandas as pd                                         # DataFrames\n",
    "import matplotlib.pyplot as plt                             # for plotting\n",
    "import geostatspy.GSLIB as GSLIB                            # for visualizing gridded data"
   ]
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    "If you get a package import error, you may have to first install some of these packages. This can usually be accomplished by opening up a command window on Windows and then typing `python -m pip install [package-name]`. More assistance is available with the respective package docs.  \n",
    "\n"
   ]
  },
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   "metadata": {},
   "source": [
    "#### Set the working directory\n",
    "\n",
    "I always like to do this so I don't lose files and to simplify subsequent read and writes (avoid including the full address each time).  Also, in this case make sure to place the required (see below) data file in this directory.  When we are finished with this tutorial we will write our new dataset back to this directory.  "
   ]
  },
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   "execution_count": 6,
   "metadata": {},
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   "source": [
    "#os.chdir(\"c:/PGE383\")                                       # set the working directory"
   ]
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    "#### Loading Tabular Data \n",
    "\n",
    "Let's load the provided multivariate, spatial dataset.  '2D_MV_200wells.csv' is available [here](https://github.com/GeostatsGuy/GeoDataSets/blob/master/2D_MV_200wells.csv).  It is a comma delimited file with: \n",
    "\n",
    "* X and Y coordinates ($m$)\n",
    "* facies 1 and 2 (1 is sandstone and 2 interbedded sand and mudstone)\n",
    "* porosity (fraction)\n",
    "* permeability ($mD$)\n",
    "* acoustic impedance ($\\frac{kg}{m^3} \\cdot \\frac{m}{s} \\cdot 10^6$). \n",
    "\n",
    "We load it with the pandas 'read_csv' function into a data frame we called 'df' and then preview it by printing a slice and by utilizing the 'head' DataFrame member function (with a nice and clean format, see below).\n",
    "\n",
    "**Python Tip: using functions from a package** just type the label for the package that we declared at the beginning:\n",
    "\n",
    "```python\n",
    "import pandas as pd\n",
    "```\n",
    "\n",
    "so we can access the pandas function 'read_csv' with the command: \n",
    "\n",
    "```python\n",
    "pd.read_csv()\n",
    "```\n",
    "\n",
    "but read csv has required input parameters. The essential one is the name of the file. For our circumstance all the other default parameters are fine. If you want to see all the possible parameters for this function, just go to the docs [here](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html).  \n",
    "\n",
    "* The docs are always helpful\n",
    "* There is often a lot of flexibility for Python functions, possible through using various inputs parameters\n",
    "\n",
    "also, the program has an output, a pandas DataFrame loaded from the data.  So we have to specficy the name / variable representing that new object.\n",
    "\n",
    "```python\n",
    "df = pd.read_csv(\"2D_MV_200wells.csv\")  \n",
    "```\n",
    "\n",
    "Let's run this command to load the data and then look at the resulting DataFrame to ensure that we loaded it.  But let's through a wrench in the cogs - let's use the wrong name.\n",
    "\n",
    "* We will get an error and learn something new about Jupyter Notebooks with Python.  Run this line and skim quickly through the result."
   ]
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     "ename": "FileNotFoundError",
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      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-7-d8bdc691c423>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"WRONG_NAME.csv\"\u001b[0m\u001b[1;33m)\u001b[0m                      \u001b[1;31m# read a .csv file in as a DataFrame\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
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      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    446\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    447\u001b[0m     \u001b[1;31m# Create the parser.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 448\u001b[1;33m     \u001b[0mparser\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfp_or_buf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    449\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    450\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m    878\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    879\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 880\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    881\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    882\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[1;34m(self, engine)\u001b[0m\n\u001b[0;32m   1112\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"c\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1113\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"c\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1114\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1115\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1116\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"python\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, src, **kwds)\u001b[0m\n\u001b[0;32m   1889\u001b[0m         \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"usecols\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0musecols\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1890\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1891\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1892\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munnamed_cols\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munnamed_cols\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1893\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] File WRONG_NAME.csv does not exist: 'WRONG_NAME.csv'"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv(\"WRONG_NAME.csv\")                      # read a .csv file in as a DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**That was ugly!** if you haven't coded before, this result is quite overwhelming. \n",
    "\n",
    "* What happenned? \n",
    "\n",
    "There was a error of course, but Jupyter assumes that you are a 'coder' and so it printed out a **trace**.  A trace is the flow of the code in to all the packages, functions, subfunctions until the error happenned.  This is very useful if you are trying to debug.  If you are not debugging, then the best thing to do is:\n",
    "\n",
    "* to skip to the end to see the error at the end\n",
    "* you could also look at the top to see which line of your code had the error\n",
    "\n",
    "Ok, enough of that let's really load the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df = pd.read_csv(\"12_sample_data.csv\")                      # read a .csv file in as a DataFrame\n",
    "df = pd.read_csv(\"https://raw.githubusercontent.com/GeostatsGuy/GeoDataSets/master/12_sample_data.csv\") # load data from Dr. Pyrcz's GitHub respository"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It worked!  No errors.  But how do you know?  \n",
    "\n",
    "* Let's take a quick look at the DataFrame with our data known as 'df'\n",
    "\n",
    "We have a build in function with data frames to provide a preview of the first 'n' columns.\n",
    "\n",
    "Let's try that out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Facies</th>\n",
       "      <th>Porosity</th>\n",
       "      <th>Perm</th>\n",
       "      <th>AI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>140.021266</td>\n",
       "      <td>3413.063944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>50.0</td>\n",
       "      <td>850.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.237154</td>\n",
       "      <td>39.837129</td>\n",
       "      <td>3074.562617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>50.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.234352</td>\n",
       "      <td>84.992437</td>\n",
       "      <td>2292.783358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>50.0</td>\n",
       "      <td>750.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.244553</td>\n",
       "      <td>90.632307</td>\n",
       "      <td>2494.848885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>50.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.231787</td>\n",
       "      <td>811.547979</td>\n",
       "      <td>2522.063995</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0     X      Y  Facies  Porosity        Perm           AI\n",
       "0           1  50.0  900.0     1.0  0.220761  140.021266  3413.063944\n",
       "1           2  50.0  850.0     1.0  0.237154   39.837129  3074.562617\n",
       "2           3  50.0  800.0     1.0  0.234352   84.992437  2292.783358\n",
       "3           4  50.0  750.0     1.0  0.244553   90.632307  2494.848885\n",
       "4           5  50.0  700.0     1.0  0.231787  811.547979  2522.063995"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()                                                   # preview the first 5 rows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have demonstrated the use of a default parameter in a function.  The head() command has a parameter 'n' for number of rows that is default as 5.  \n",
    "\n",
    "* let's try changing that to another number\n",
    "\n",
    "```python\n",
    "df.head(n=13)\n",
    "```\n",
    "\n",
    "would result in 13 rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Facies</th>\n",
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       "      <th>Perm</th>\n",
       "      <th>AI</th>\n",
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       "      <td>3413.063944</td>\n",
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       "      <td>3074.562617</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>50.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>84.992437</td>\n",
       "      <td>2292.783358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>50.0</td>\n",
       "      <td>750.0</td>\n",
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       "      <td>90.632307</td>\n",
       "      <td>2494.848885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>50.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.231787</td>\n",
       "      <td>811.547979</td>\n",
       "      <td>2522.063995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>50.0</td>\n",
       "      <td>650.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.233280</td>\n",
       "      <td>426.992456</td>\n",
       "      <td>3964.185956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8</td>\n",
       "      <td>50.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.234423</td>\n",
       "      <td>2398.406492</td>\n",
       "      <td>3318.885844</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9</td>\n",
       "      <td>50.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.219657</td>\n",
       "      <td>1637.224971</td>\n",
       "      <td>3030.874323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10</td>\n",
       "      <td>50.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.200389</td>\n",
       "      <td>265.636019</td>\n",
       "      <td>3454.389302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12</td>\n",
       "      <td>50.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.165908</td>\n",
       "      <td>7.951511</td>\n",
       "      <td>5025.286221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>13</td>\n",
       "      <td>50.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.155199</td>\n",
       "      <td>1.476967</td>\n",
       "      <td>4939.312852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>16</td>\n",
       "      <td>50.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.175103</td>\n",
       "      <td>5.476548</td>\n",
       "      <td>4770.744199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>19</td>\n",
       "      <td>100.0</td>\n",
       "      <td>950.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.202124</td>\n",
       "      <td>357.481641</td>\n",
       "      <td>3383.118632</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0      X      Y  Facies  Porosity         Perm           AI\n",
       "0            1   50.0  900.0     1.0  0.220761   140.021266  3413.063944\n",
       "1            2   50.0  850.0     1.0  0.237154    39.837129  3074.562617\n",
       "2            3   50.0  800.0     1.0  0.234352    84.992437  2292.783358\n",
       "3            4   50.0  750.0     1.0  0.244553    90.632307  2494.848885\n",
       "4            5   50.0  700.0     1.0  0.231787   811.547979  2522.063995\n",
       "5            6   50.0  650.0     1.0  0.233280   426.992456  3964.185956\n",
       "6            8   50.0  550.0     1.0  0.234423  2398.406492  3318.885844\n",
       "7            9   50.0  500.0     1.0  0.219657  1637.224971  3030.874323\n",
       "8           10   50.0  450.0     1.0  0.200389   265.636019  3454.389302\n",
       "9           12   50.0  350.0     0.0  0.165908     7.951511  5025.286221\n",
       "10          13   50.0  300.0     0.0  0.155199     1.476967  4939.312852\n",
       "11          16   50.0  150.0     0.0  0.175103     5.476548  4770.744199\n",
       "12          19  100.0  950.0     1.0  0.202124   357.481641  3383.118632"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(n=13)                                               # preview the first 5 rows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Summary Statistics\n",
    "\n",
    "Let's get some summary statistics to assist with plotting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>10%</th>\n",
       "      <th>50%</th>\n",
       "      <th>90%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <td>480.0</td>\n",
       "      <td>402.064583</td>\n",
       "      <td>248.064403</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>62.800000</td>\n",
       "      <td>412.000000</td>\n",
       "      <td>741.100000</td>\n",
       "      <td>826.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X</th>\n",
       "      <td>480.0</td>\n",
       "      <td>430.187500</td>\n",
       "      <td>263.832692</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>390.000000</td>\n",
       "      <td>830.000000</td>\n",
       "      <td>980.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y</th>\n",
       "      <td>480.0</td>\n",
       "      <td>522.166667</td>\n",
       "      <td>284.293420</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>139.000000</td>\n",
       "      <td>539.000000</td>\n",
       "      <td>900.000000</td>\n",
       "      <td>999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Facies</th>\n",
       "      <td>480.0</td>\n",
       "      <td>0.616667</td>\n",
       "      <td>0.486706</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Porosity</th>\n",
       "      <td>480.0</td>\n",
       "      <td>0.189440</td>\n",
       "      <td>0.031702</td>\n",
       "      <td>0.117562</td>\n",
       "      <td>0.149089</td>\n",
       "      <td>0.185443</td>\n",
       "      <td>0.233571</td>\n",
       "      <td>0.261091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Perm</th>\n",
       "      <td>480.0</td>\n",
       "      <td>520.932093</td>\n",
       "      <td>1226.207190</td>\n",
       "      <td>0.005776</td>\n",
       "      <td>0.986799</td>\n",
       "      <td>49.451463</td>\n",
       "      <td>1553.194729</td>\n",
       "      <td>10319.904849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AI</th>\n",
       "      <td>480.0</td>\n",
       "      <td>3758.879653</td>\n",
       "      <td>779.990582</td>\n",
       "      <td>1746.387548</td>\n",
       "      <td>2773.024500</td>\n",
       "      <td>3719.883000</td>\n",
       "      <td>4801.380078</td>\n",
       "      <td>6194.573653</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            count         mean          std          min          10%  \\\n",
       "Unnamed: 0  480.0   402.064583   248.064403     1.000000    62.800000   \n",
       "X           480.0   430.187500   263.832692     0.000000   100.000000   \n",
       "Y           480.0   522.166667   284.293420    19.000000   139.000000   \n",
       "Facies      480.0     0.616667     0.486706     0.000000     0.000000   \n",
       "Porosity    480.0     0.189440     0.031702     0.117562     0.149089   \n",
       "Perm        480.0   520.932093  1226.207190     0.005776     0.986799   \n",
       "AI          480.0  3758.879653   779.990582  1746.387548  2773.024500   \n",
       "\n",
       "                    50%          90%           max  \n",
       "Unnamed: 0   412.000000   741.100000    826.000000  \n",
       "X            390.000000   830.000000    980.000000  \n",
       "Y            539.000000   900.000000    999.000000  \n",
       "Facies         1.000000     1.000000      1.000000  \n",
       "Porosity       0.185443     0.233571      0.261091  \n",
       "Perm          49.451463  1553.194729  10319.904849  \n",
       "AI          3719.883000  4801.380078   6194.573653  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(percentiles=[0.1,0.9]).transpose()            # summary statistics, including P10 and P90"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we can observed a reasoanble minumum and maximum for plotting porosity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "pormin = 0.1; pormax = 0.28\n",
    "xmin = 0; xmax = 1000\n",
    "ymin = 0; ymax = 1000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plotting Tabular Data\n",
    "\n",
    "Let's make some simple plots for our tabular data, porosity location map and histogram."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(121)\n",
    "GSLIB.locmap_st(df,'X','Y','Porosity',xmin,xmax,ymin,ymax,pormin,pormax,'Location Map','X(m)','Y(m)','Porosity (fraction)',cmap)\n",
    "\n",
    "plt.subplot(122)\n",
    "GSLIB.hist_st(df['Porosity'].values,pormin,pormax,log=False,cumul = False,bins=20,weights = None, xlabel='Porosity (fraction)',title='Histogram')\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=1.1, wspace=0.2, hspace=0.3); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Loading Gridded Data \n",
    "\n",
    "Let's load a comma delimited file with a mesh of accoustic impedance values over the same volume as the wells above.  \n",
    "\n",
    "* There are many formats of gridded data\n",
    "\n",
    "* We have a simple 2D matrix of comma delimited values for this demonstration\n",
    "\n",
    "* For examples of loading and saving with various gridded data formats see this workflow [here](https://git.io/fNgRu).\n",
    "\n",
    "We will use the NumPy package object 'ndarray' which is a multidimensional array (we will work in 2D).  \n",
    "\n",
    "* an array is simply a list of values\n",
    "\n",
    "* a 2D array represents the spatial property values over a regularly spaced mesh\n",
    "\n",
    "We can use the command:\n",
    "\n",
    "```python \n",
    "seismic = np.loadtxt()\n",
    "```\n",
    "\n",
    "We just need to specify two parameters:\n",
    "\n",
    "* **fname** - the name of the file, it will look in our current directory\n",
    "* **delimiter** - the symbol used between the values, we are comma delimited"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "seismic = np.loadtxt(fname = \"12_AI.csv\", delimiter=\",\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No errors?\n",
    "\n",
    "Then we are successful. How do we know?  \n",
    "\n",
    "* in Python you can see the object by just writing it's name.\n",
    "\n",
    "```python\n",
    "seismic\n",
    "```\n",
    "\n",
    "Let's try that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2533.88411965, 2394.78844564, 3190.23811052, ..., 4790.1473788 ,\n",
       "        4938.2651136 , 5767.83234538],\n",
       "       [3410.80188769, 3332.28652952, 2920.84106359, ..., 4718.10321317,\n",
       "        4687.60324279, 5366.89655662],\n",
       "       [3413.93007127, 3122.65470818, 3165.10983075, ..., 4571.87949876,\n",
       "        4761.73382479, 5101.28972654],\n",
       "       ...,\n",
       "       [5481.00285812, 4566.41671063, 4844.56047183, ..., 4386.33791583,\n",
       "        4869.916299  , 3984.28940954],\n",
       "       [5461.13876074, 4673.12397734, 4673.46120902, ..., 3797.13493789,\n",
       "        4443.05424623, 4837.20002239],\n",
       "       [4666.5963451 , 4280.36960363, 4687.97413979, ..., 3461.51663097,\n",
       "        3933.29016965, 4128.18118906]])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seismic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python protects you from yourself.  It would be messy to look at 100 x 100 = 10,000 values.  \n",
    "\n",
    "* So the output uses $\\dots$ to indicate not all values are shown, just the corners are shown.\n",
    "\n",
    "Of course, this is a very impractical way to look at gridded data.\n",
    "\n",
    "#### Visualization of Gridded Data\n",
    "\n",
    "Let's look at the dataset that we loaded. \n",
    "\n",
    "* We could work with MatPlotLib package directly (common data visualization package for Python) \n",
    "\n",
    "* There is a lot to learn, a bit of a hurdle\n",
    "\n",
    "* We will use the *pixelplt* reimplimentation from the GeostatsPy package.  \n",
    "\n",
    "This function uses MatPlotLib with the function parameters to build a nice figure, so we can  procastinate learning MatPlotLib for now!  \n",
    "\n",
    "* First let's set some parameters, including the spatial limits of the plot, the cell sizes in the plot and the min and max feature values and color map for the color bar.  \n",
    "\n",
    "Our regular grid is 100 x 100 cells of 10 m cells (i.e. squares), 1,000 x 1,000 m in extents and we assume the origin, low left corder is at coordinate 0,0.  Our porosity values are contained within the interval between 4 to 16%. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "xmin = 0.0;xmax = 1000.0; ymin = 0.0; ymax = 1000.0; cell_size = 10.0; \n",
    "vmin = 4.0; vmax = 16.0; cmap = plt.cm.inferno "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are ready to plot the 2D array with the pixpelplt reimplementation from our GSLIB in Python."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "GSLIB.pixelplt_st(seismic,xmin,xmax,ymin,ymax,cell_size,1500,6000,\n",
    "    \"Accoustic Impedance\",\"X(m)\",\"Y(M)\",\"Acoustic Impedance ($kg/m^3 \\cdot m/s \\cdot 10^3$)\",cmap)\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=1.0, top=1.1, wspace=0.2, hspace=0.3); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Writing the Tabular Data to a File\n",
    "\n",
    "It may be useful to write the DataFrame out for storage or curation and / or to be utilize with another platform (even R or Excel!).  It is easy to write the DataFrame back to a comma delimited file.  We have the 'to_csv' DataFrame member function to accomplish this.  The file will write to the working directory (another reason we set that at the beginning).  Go to that folder and open this new file with TextPad, Excel or any other program that opens .txt files to check it out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"12_sample_data_v2.csv\")                      # write out the df DataFrame to a comma delimited file "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Writing the Gridded Data to a File\n",
    "\n",
    "Likewise we may want to save our gridded data to a file. For example, we made have completed some data processing, clean up or made a new gridded data set (e.g. a new model).  \n",
    "\n",
    "* We can use this command:\n",
    "\n",
    "```python\n",
    "np.savetxt()\n",
    "```\n",
    "\n",
    "We just need to specify:\n",
    "\n",
    "* **fname** - the filename\n",
    "* **X** - the ndarray to save\n",
    "* **delimiter** - the character used to separate the values on a row\n",
    "\n",
    "Let's save the grid as a new version 'v2' just to demonstrate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savetxt(fname=\"12_AI_v2.csv\", X=seismic, delimiter=\",\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### More Exercises\n",
    "\n",
    "There are so many more exercises and tests that one could attempt to gain experience with the pandas (DataFrames) for tabular data and numpy (ndarrays) for gridded data packages in Python. \n",
    "\n",
    "Check out the docs: \n",
    "\n",
    "* [pandas](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html)\n",
    "\n",
    "* [numpy](https://docs.scipy.org/doc/numpy/reference/)\n",
    "\n",
    "I'm always happy to discuss,\n",
    "\n",
    "*Michael*\n",
    "\n",
    "#### The Author:\n",
    "\n",
    "### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "*Novel Data Analytics, Geostatistics and Machine Learning Subsurface Solutions*\n",
    "\n",
    "With over 17 years of experience in subsurface consulting, research and development, Michael has returned to academia driven by his passion for teaching and enthusiasm for enhancing engineers' and geoscientists' impact in subsurface resource development. \n",
    "\n",
    "For more about Michael check out these links:\n",
    "\n",
    "#### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n",
    "\n",
    "#### Want to Work Together?\n",
    "\n",
    "I hope this content is helpful to those that want to learn more about subsurface modeling, data analytics and machine learning. Students and working professionals are welcome to participate.\n",
    "\n",
    "* Want to invite me to visit your company for training, mentoring, project review, workflow design and / or consulting? I'd be happy to drop by and work with you! \n",
    "\n",
    "* Interested in partnering, supporting my graduate student research or my Subsurface Data Analytics and Machine Learning consortium (co-PIs including Profs. Foster, Torres-Verdin and van Oort)? My research combines data analytics, stochastic modeling and machine learning theory with practice to develop novel methods and workflows to add value. We are solving challenging subsurface problems!\n",
    "\n",
    "* I can be reached at mpyrcz@austin.utexas.edu.\n",
    "\n",
    "I'm always happy to discuss,\n",
    "\n",
    "*Michael*\n",
    "\n",
    "Michael Pyrcz, Ph.D., P.Eng. Associate Professor The Hildebrand Department of Petroleum and Geosystems Engineering, Bureau of Economic Geology, The Jackson School of Geosciences, The University of Texas at Austin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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